AI use cases, opportunities and challenges in the UK energy sector

From smart meters to smart resource management, Ed Watal gives a guided tour around artificial applications at grid and consumer level, to better understand how algorithms are increasingly running our power supplies. 

Many of the worst environmental catastrophes we face today, including those here in the UK, are the direct result of the energy industry. Fossil-fuel-burning power plants are the second largest emitter of greenhouse gas emissions and have many other consequences, including the emission of airborne mercury. Understandably, there has been an increased push for renewable energy, but this transition is easier said than done, considering how dependent our infrastructure is on fossil fuels.

Chief among the obstacles to adopting renewable energy is that power grids become significantly more difficult to manage when additional power sources — particularly from non-traditional sources, such as renewable energy — are introduced. Thankfully, innovations in technology such as artificial intelligence have been introduced, allowing UK energy companies to manage their production and output more effectively, serving as an essential first step toward a greener future.

How artificial intelligence enables smart resource management in the energy sector
Artificial intelligence technology is poised to ignite a revolution in the UK’s energy sector thanks to two main capabilities: data analysis and predictive analytics. When combined, these two functions of AI allow people to effectively pursue smart resource management in ways that were once thought impossible and enable the energy sector to significantly curb the negative environmental impact it is causing.

At its core, smart resource management is an approach to resource use — including energy — that aims to maximize output while minimizing cost and environmental impact. Artificial intelligence models can enable UK homeowners and businesses to achieve smart resource management by providing unprecedented levels of insight into energy consumption and efficiency.

Understanding smart resource management in the energy sector
For those hoping to achieve smart resource management, there are three essential qualities to prioritize. Reducing cost and environmental impact of operations – the fundamental aspect of smart resource management is reducing operations’ cost and environmental impact. Companies can use artificial intelligence to better manage the use and production of power, thereby minimizing overproduction.

Minimizing operational and supply risks – smart resource management also requires producers and consumers to minimize the risk of disruption. One of the reasons why renewable energy has not yet been embraced on a wider scale is its relatively unreliable nature. For example, solar power grids cannot produce electricity at night or in severely cloudy situations, and wind fields are less efficient in times of low wind. AI predictive analytics can allow resource companies to forecast the productivity of their renewable energy sources, allowing them to adjust production and use to account for these changes.

Capturing new growth opportunities – the final aspect of smart resource management is the ability to grow sustainably. Using artificial intelligence and its predictive analytics capabilities, renewable energy companies can more effectively identify potential sites for development. For example, a solar power company in the UK can use an AI model to analyze data about climate, sun exposure, and other factors to determine ideal locations for new arrays.

Smart meters: A groundbreaking use case for AI in the energy sector
The most obvious use case of artificial intelligence in smart resource management is the smart meter. Like traditional power meters, a smart meter’s primary purpose is to track a building’s power consumption, but smart meters can also provide real-time insights to both occupants and energy companies into the real-time energy use of that building. This data can then be analyzed using artificial intelligence to improve the efficiency of energy production and use.

By providing better real-time insights into data use, smart meters have the potential to curb the UK’s overall energy consumption significantly. AI models can give suggestions to both individual homeowners and energy providers using smart meter data to help them improve their energy consumption habits.

However, the benefits of analyzing smart meter data using AI don’t stop with sustainability — UK consumers and energy companies alike will be rewarded with savings for improving the efficiency of their energy consumption. According to some experts, individuals could see as much as 30% savings if they follow the energy efficiency recommendations of artificial intelligence using smart meter data, meaning homeowners are doubly incentivized to improve their homes.

How UK regulation affects AI use in the energy sector
UK landowners may also be required to implement some of these improvements if they hope to rent out their properties. The regulations known as the Minimum Energy Efficiency Standards (MEES) require commercial properties — both residential and non-residential — to meet guidelines of energy efficiency and carbon emissions or face a substantial fine. Analytics powered by artificial intelligence can help guide owners to make wiser, more efficient decisions regarding their energy efficiency.

It’s important to remember that UK regulations are based around a seven-year “payback period,” which means landlords are only expected to implement improvements that will lead to cost savings greater than their investment after seven years. In other words, homeowners’ investments in their eco-friendliness will pay for themselves.

AI models can identify anomalies in energy consumption almost immediately, allowing landowners to repair systems and get consumption levels back on track quickly. Systems can also be analyzed to determine when preventative maintenance may be required, allowing owners to ensure systems are consistently operating at peak efficiency.

Still, it isn’t only environmental regulations that companies using AI to improve their efficiency must consider. Companies must also ensure they maintain compliance with any regulations that may be in place regarding the use of artificial intelligence technology. Recently, the UK introduced a framework for AI use designed to encourage technology innovation while protecting the safety of all involved. Although this framework is not yet codified into law, it establishes an expectation of data privacy, transparency, and fairness that energy companies will be expected to follow.

It is more apparent than ever before that something needs to be done about the environmental destruction caused by the energy industry — and soon. Thankfully, artificial intelligence provides a very real solution to many of the problems the energy industry suffers, as it will allow both consumers and producers to achieve smart resource management at levels hitherto unattainable. Although the energy sector is historically reluctant to embrace new technological paradigms, the potential implications of artificial intelligence on sustainability must not be ignored.

Ed Watal is an AI thought leader, technology Investor and founder of business intelligence specialist, Intellibus. 

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